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The code is based on the Barnes–Hut algorithm and runs cosmological N-body simulations on parallel machines with a distributed memory architecture using the MPI message-passing library.
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The parallel code exploits the architecture of the computational clusters using the MPI (Message Passing Interface).
The distributed memory parallel programming model is examined for simulation programs using the MPI message passing interface.
Maximum likelihood inference was carried out in RA×ML 7.0.4 [40] at the AMNH Computational Sciences facility on an 8-way server with 2.2 GHz AMD Opteron 846 processors and 128 GB RAM using the fine-grained parallel Pthreads (POSIX Threads Library; [41]) and on the CIPRES cluster (http://www.phylo.org) using the MPI (Message Passing Interface; [42], [43]) implementations.
The purpose of this example was to show parallel computing using the MPI (Message Passing Interface) library.
Parallel computing can be achieved by programming with C, C++, or Fortran, e.g., using the MPI (Message Passing Interface) library to handle inter-process communication [ 6].
Communication over Myrinet is achieved with an installation of the MPI message-passing standard, though MPI can also be directed to use Ethernet instead.
Optionally, Meep is able to run on a distributed-memory parallel machine, and to do this we use the standard MPI message-passing interface.
Parallel applications in the area of scientific computing are often designed in a data parallel SPMD (single program multiple data) style based on the MPI (message passing interface) standard.
Both integral generation and the linear-scaling optimization procedures are parallelized using the standard MPI message passing interface mixed with an OpenMP strategy.
As a proof of concept, we introduce novel HPC software developed to control the motion of a set of robots using the standard parallel MPI (Message Passing Interface) library.
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